ASCM: An Answer Space Clustered Prompting Method without Answer Engineering

ASCM is a method for generating prompts in natural language processing tasks without requiring the manual engineering of answers. The method involves clustering prompts based on their semantic similarity and selecting a representative prompt from each cluster. These representative prompts are then used to generate responses from a language model.

The key advantage of ASCM is that it does not require the manual annotation of answers, which can be time-consuming and expensive. Instead, it leverages the natural clustering of prompts to generate responses. This makes it particularly useful in settings where the range of possible answers is large and difficult to enumerate.

ASCM has been successfully applied in a variety of natural language processing tasks, including dialogue systems, question answering, and text classification. It has been shown to outperform other methods that rely on answer engineering, particularly in settings where the range of possible answers is large and complex.